I don't understand the math completely but it looks like dropout can be derived from a Gaussian prior (approximating the Bernoulli) in a Bayesian context. One

I don't understand the math completely but it looks like dropout can be derived from a Gaussian prior (approximating the Bernoulli) in a Bayesian context. One useful tidbit is that you can get prediction intervals from deep learning models by running it forward N times with dropout and take the mean and variance of that distribution (plus another precision term).